Enhanced Prediction of Atopic Dermatitis Severity Using Advanced Machine Learning Techniques

Atopic dermatitis (AD) is a chronic inflammatory skin condition with increasing prevalence in Indonesia, presenting significant challenges in diagnosis and management. This study introduces a novel approach to objectively classify AD severity using advanced machine learning techniques and comprehensive color feature extraction from clinical images. A unique dataset of 3,037 high-resolution photographs from 250 Acehnese patients was collected at RSUD Dr. Zainoel Abidin, captured using a standardized 12-megapixel smartphone protocol. Ninety color features were extracted from multiple color spaces to capture the diverse visual characteristics of AD lesions. Four state-of-the-art machine learning models - XGBoost, CatBoost, LightGBM, and Random Forest - were employed to classify AD severity into four categories: None, Mild, Moderate, and Severe. Models were trained and evaluated using various performance metrics including accuracy, precision, recall, and F1-score. Results showed high performance across all models, with LightGBM achieving the highest accuracy of 95.07%, followed closely by XGBoost at 94.74%. SHAP analysis revealed that standard deviation features, particularly those related to hue and chrominance variability across different color spaces, were most influential in severity classification. This research demonstrates the potential of combining advanced color feature extraction with machine learning to create an objective, consistent, and accessible tool for AD severity assessment. The findings have significant implications for improving AD management in resource-limited settings and standardizing severity assessment in clinical practice and research